Safe Bets, Long Shots, and Toss-Ups: Strategic Engagements Between Activists and Firms
Bibliographic record
Abstract
We use a game-theoretic model to examine how different types of activist motivation affect strategic interactions between an activist and a firm in the context of a threatened adversarial engagement, in which the activist can benefit from “warm glow” and media publicity as well as from firm compliance with activist demands. The model yields novel predictions about when firms prefer to self-regulate to pre-empt a contested engagement, how vigorously firms defend themselves against the activist’s attack if an engagement occurs, and a new taxonomy of engagements, characterized by offensive and defensive strategies and the likelihood of activist success. The model predicts that when warm glow and campaign-driven wins are important motivations for activists, safe bet and long shot types of engagements are more likely to occur: These tend to be lower expenditure skirmishes where one party has a clear advantage and where a pre-emptive settlement is infeasible. By contrast, firms and activists are more likely to negotiate self-regulation that pre-empts resource-intensive toss-up engagements where each side is evenly matched and expends significant effort. Our findings contribute to strategic management research by developing new insights about how firms respond to different activist motivations and types of engagements. We explore extensions of the model and discuss implications for future empirical and theoretical research on the management of activist relations. This paper was accepted by Joshua Gans, business strategy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.01638 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".